Yeast Two-Hybrid Screening of Photoswitchable Protein-Protein Interaction Libraries

CollaPPI: A Collaborative Learning Framework for Predicting Protein-Protein Interactions

Exploring protein-protein interaction (PPI) is of paramount importance for elucidating the intrinsic mechanism of various biological processes. Nevertheless, experimental determination of PPI can be both time-consuming and expensive, motivating the exploration of data-driven deep learning technologies as a viable, efficient, and accurate alternative. Nonetheless, most current deep learning-based methods regarded a pair of proteins to be predicted for possible interaction as two separate entities when extracting PPI features, thus neglecting the knowledge sharing among the collaborative protein and the target protein. Aiming at the above issue, a collaborative learning framework CollaPPI was proposed in this study, where two kinds of collaboration, i.e., protein-level collaboration and task-level collaboration, were incorporated to achieve not only the knowledge-sharing between a pair of proteins, but also the complementation of such shared knowledge between biological domains closely related to PPI (i.e., protein function, and subcellular location). Evaluation results demonstrated that CollaPPI obtained superior performance compared to state-of-the-art methods on two PPI benchmarks. Besides, evaluation results of CollaPPI on the additional PPI type prediction task further proved its excellent generalization ability.

Predicting Protein-Protein Interactions Using Sequence and Network Information via Variational Graph Autoencoder

Protein-protein interactions (PPIs) play a critical role in the proteomics study, and a variety of computational algorithms have been developed to predict PPIs. Though effective, their performance is constrained by high false-positive and false-negative rates observed in PPI data. To overcome this problem, a novel PPI prediction algorithm, namely PASNVGA, is proposed in this work by combining the sequence and network information of proteins via variational graph autoencoder. To do so, PASNVGA first applies different strategies to extract the features of proteins from their sequence and network information, and obtains a more compact form of these features using principal component analysis. In addition, PASNVGA designs a scoring function to measure the higher-order connectivity between proteins and so as to obtain a higher-order adjacency matrix. With all these features and adjacency matrices, PASNVGA trains a variational graph autoencoder model to further learn the integrated embeddings of proteins. The prediction task is then completed by using a simple feedforward neural network. Extensive experiments have been conducted on five PPI datasets collected from different species. Compared with several state-of-the-art algorithms, PASNVGA has been demonstrated as a promising PPI prediction algorithm.

Combination of in vivo proximity labeling and co-immunoprecipitation identifies the host target network of a tumor-inducing effector in the fungal maize pathogen Ustilago maydis

Plant pathogens secrete effectors, which target host proteins to facilitate infection. The Ustilago maydis effector UmSee1 is required for tumor formation in the leaf during infection of maize. UmSee1 interacts with maize SGT1 (suppressor of G2 allele of skp1) and blocks its phosphorylation in vivo. In the absence of UmSee1, U. maydis cannot trigger tumor formation in the bundle sheath. However, it remains unclear which host processes are manipulated by UmSee1 and the UmSee1-SGT1 interaction to cause the observed phenotype. Proximity-dependent protein labeling involving the turbo biotin ligase tag (TurboID) for proximal labeling of proteins is a powerful tool for identifying the protein interactome. We have generated transgenic U. maydis that secretes biotin ligase-fused See1 effector (UmSee1-TurboID-3HA) directly into maize cells. This approach, in combination with conventional co-immunoprecipitation, allowed the identification of additional UmSee1 interactors in maize cells. Collectively, our data identified three ubiquitin-proteasome pathway-related proteins (ZmSIP1, ZmSIP2, and ZmSIP3) that either interact with or are close to UmSee1 during host infection of maize with U. maydis. ZmSIP3 represents a cell cycle regulator whose degradation appears to be promoted in the presence of UmSee1. Our data provide a possible explanation of the requirement for UmSee1 in tumor formation during U. maydis-Zea mays interaction.

Engineering of bidirectional, cyanobacteriochrome-based light-inducible dimers (BICYCL)s

Optogenetic tools for controlling protein-protein interactions (PPIs) have been developed from a small number of photosensory modules that respond to a limited selection of wavelengths. Cyanobacteriochrome (CBCR) GAF domain variants respond to an unmatched array of colors; however, their natural molecular mechanisms of action cannot easily be exploited for optogenetic control of PPIs. Here we developed bidirectional, cyanobacteriochrome-based light-inducible dimers (BICYCL)s by engineering synthetic light-dependent interactors for a red/green GAF domain. The systematic approach enables the future engineering of the broad chromatic palette of CBCRs for optogenetics use. BICYCLs are among the smallest optogenetic tools for controlling PPIs and enable either green-ON/red-OFF (BICYCL-Red) or red-ON/green-OFF (BICYCL-Green) control with up to 800-fold state selectivity. The access to green wavelengths creates new opportunities for multiplexing with existing tools. We demonstrate the utility of BICYCLs for controlling protein subcellular localization and transcriptional processes in mammalian cells and for multiplexing with existing blue-light tools.

Screening and characterisation of proteins interacting with the mitogen-activated protein kinase Crmapk in the fungus Clonostachys chloroleuca

Clonostachys chloroleuca 67-1 (formerly C. rosea 67-1) is a promising mycoparasite with great potential for controlling various plant fungal diseases. The mitogen-activated protein kinase (MAPK)-encoding gene Crmapk is of great importance to the mycoparasitism and biocontrol activities of C. chloroleuca. To investigate the molecular mechanisms underlying the role of Crmapk in mycoparasitism, a high-quality yeast two hybrid (Y2H) library of C. chloroleuca 67-1 was constructed, and proteins interacting with Crmapk were characterised. The library contained 1.6 × 10⁷ independent clones with a recombination rate of 96%, and most inserted fragments were > 1 kb. The pGBKT7-Crmapk bait vector with no self-activation or toxicity to yeast cells was used to screen interacting proteins from the Y2H library, resulting in 60 candidates, many linked to metabolism, cellular processes and signal transduction. Combined bioinformatics and transcriptome analyses of C. chloroleuca 67-1 and ΔCrmapk mutant mycoparasitising Sclerotinia sclerotiorum sclerotia, 41 differentially expressed genes were identified, which might be the targets of the Fus3/Kss1-MAPK pathway. The results provide a profile of potential protein interactions associated with MAPK enzymes in mycoparasites, and are of great significance for understanding the mechanisms of Crmapk regulating C. chloroleuca mycoparasitism.

Directed evolution approaches for optogenetic tool development

Photoswitchable proteins enable specific molecular events occurring in complex biological settings to be probed in a rapid and reversible fashion. Recent progress in the development of photoswitchable proteins as components of optogenetic tools has been greatly facilitated by directed evolution approaches in vitro, in bacteria, or in yeast. We review these developments and suggest future directions for this rapidly advancing field.

Construction of ferritin hydrogels utilizing subunit-subunit interactions

Various proteins form nanostructures exhibiting unique functions, making them attractive as next-generation materials. Ferritin is a hollow spherical protein that incorporates iron ions. Here, we found that hydrogels are simply formed from concentrated apoferritin solutions by acid denaturation and subsequent neutralization. The water content of the hydrogel was approximately 80%. The apoferritin hydrogel did not decompose in the presence of 1 M HCl, 2-mercaptoethanol, or methanol but was dissolved in the presence of 1 M NaOH, by heating at 80°C, or by treatment with trypsin or 6 M guanidine hydrochloride. The Young’s modulus of the hydrogel was 20.4 ± 12.1 kPa according to local indentation experimentes using atomic force microscopy, indicating that the hydrogel was relatively stiff. Transition electron microscopy measurements revealed that a fibrous network was constructed in the hydrogel. The color of the hydrogel became yellow-brown upon incubation in the presence of Fe³⁺ ions, indicating that the hydrogel adsorbed the Fe³⁺ ions. The yellow-brown color of the Fe³⁺-adsorbed hydrogel did not change upon incubation in pure water, whereas it became pale by incubating it in the presence of 100 mM ethylenediaminetetraacetic acid (EDTA). The apoferritin hydrogel also adsorbed Co²⁺ and Cu²⁺ ions and released them in the presence of EDTA, while it adsorbed less Ni²⁺ ions; more Fe³⁺ ions adsorbed to the apoferritin hydrogel than other metal ions, indicating that the hydrogel keeps the iron storage characteristic of ferritin. These results demonstrate a new property of ferritin: the ability to form a hydrogel that can adsorb/desorb metal ions, which may be useful in designing future biomaterials.

Structure-based design of a photoswitchable affibody scaffold

Photo-control of affinity reagents offers a general approach for high-resolution spatiotemporal control of diverse molecular processes. In an effort to develop general design principles for a photo-controlled affinity reagent, we took a structure-based approach to the design of a photoswitchable Z-domain, among the simplest of affinity reagent scaffolds. A chimera, designated Z-PYP, of photoactive yellow protein (PYP) and the Z-domain, was designed based on the concept of mutually exclusive folding. NMR analysis indicated that, in the dark, the PYP domain of the chimera was folded, and the Z-domain was unfolded. Blue light caused loss of structure in PYP and a two- to sixfold change in the apparent affinity of Z-PYP for its target as determined using size exclusion chromatography, UV-Vis based assays, and enyzme-linked immunosorbent assay (ELISA). A thermodynamic model indicated that mutations to decrease Z-domain folding energy would alter target affinity without loss of switching. This prediction was confirmed experimentally with a double alanine mutant in helix 3 of the Z-domain of the chimera (Z-PYP-AA) showing >30-fold lower dark-state binding and no loss in switching. The effect of decreased dark-state binding affinity was tested in a two-hybrid transcriptional control format and enabled pronounced light/dark differences in yeast growth in vivo. Finally, the design was transferable to the αZ-Taq affibody enabling tunable light-dependent binding both in vitro and in vivo to the Z-Taq target. This system thus provides a framework for the focused development of light switchable affibodies for a range of targets.

Steering Molecular Activity with Optogenetics: Recent Advances and Perspectives

Optogenetics utilizes photosensitive proteins to manipulate the localization and interaction of molecules in living cells. Because light can be rapidly switched and conveniently confined to the sub-micrometer scale, optogenetics allows for controlling cellular events with an unprecedented resolution in time and space. The past decade has witnessed an enormous progress in the field of optogenetics within the biological sciences. The ever-increasing amount of optogenetic tools, however, can overwhelm the selection of appropriate optogenetic strategies. Considering that each optogenetic tool may have a distinct mode of action, a comparative analysis of the current optogenetic toolbox can promote the further use of optogenetics, especially by researchers new to this field. This review provides such a compilation that highlights the spatiotemporal accuracy of current optogenetic systems. Recent advances of optogenetics in live cells and animal models are summarized, the emerging work that interlinks optogenetics with other research fields is presented, and exciting clinical and industrial efforts to employ optogenetic strategy toward disease intervention are reported.

Delayed Comparison and Apriori Algorithm (DCAA): A Tool for Discovering Protein-Protein Interactions From Time-Series Phosphoproteomic Data

Analysis of high-throughput omics data is one of the most important approaches for obtaining information regarding interactions between proteins/genes. Time-series omics data are a series of omics data points indexed in time order and normally contain more abundant information about the interactions between biological macromolecules than static omics data. In addition, phosphorylation is a key posttranslational modification (PTM) that is indicative of possible protein function changes in cellular processes. Analysis of time-series phosphoproteomic data should provide more meaningful information about protein interactions. However, although many algorithms, databases, and websites have been developed to analyze omics data, the tools dedicated to discovering molecular interactions from time-series omics data, especially from time-series phosphoproteomic data, are still scarce. Moreover, most reported tools ignore the lag between functional alterations and the corresponding changes in protein synthesis/PTM and are highly dependent on previous knowledge, resulting in high false-positive rates and difficulties in finding newly discovered protein–protein interactions (PPIs). Therefore, in the present study, we developed a new method to discover protein–protein interactions with the delayed comparison and Apriori algorithm (DCAA) to address the aforementioned problems. DCAA is based on the idea that there is a lag between functional alterations and the corresponding changes in protein synthesis/PTM. The Apriori algorithm was used to mine association rules from the relationships between items in a dataset and find PPIs based on time-series phosphoproteomic data. The advantage of DCAA is that it does not rely on previous knowledge and the PPI database. The analysis of actual time-series phosphoproteomic data showed that more than 68% of the protein interactions/regulatory relationships predicted by DCAA were accurate. As an analytical tool for PPIs that does not rely on a priori knowledge, DCAA should be useful to predict PPIs from time-series omics data, and this approach is not limited to phosphoproteomic data.

The rise and shine of yeast optogenetics

Optogenetics refers to the control of biological processes with light. The activation of cellular phenomena by defined wavelengths has several advantages compared with traditional chemically inducible systems, such as spatiotemporal resolution, dose-response regulation, low cost, and moderate toxic effects. Optogenetics has been successfully implemented in yeast, a remarkable biological platform that is not only a model organism for cellular and molecular biology studies, but also a microorganism with diverse biotechnological applications. In this review, we summarize the main optogenetic systems implemented in the budding yeast Saccharomyces cerevisiae, which allow orthogonal control (by light) of gene expression, protein subcellular localization, reconstitution of protein activity, and protein sequestration by oligomerization. Furthermore, we review the application of optogenetic systems in the control of metabolic pathways, heterologous protein production and flocculation. We then revise an example of a previously described yeast optogenetic switch, named FUN-LOV, which allows precise and strong activation of the target gene. Finally, we describe optogenetic systems that have not yet been implemented in yeast, which could therefore be used to expand the panel of available tools in this biological chassis. In conclusion, a wide repertoire of optogenetic systems can be used to address fundamental biological questions and broaden the biotechnological toolkit in yeast.